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semantic knowledge graph Generator and Ontology Recommender: Industry Use Cases

INTERNATIONAL JOURNAL OF semantic COMPUTING 2024年第2期18卷 223-238页

作者： Ferguson, Holly Yu, Dan Kritzler, Mareike Siemens Technol 1936 Univ Ave Berkeley CA 94704 USA Mentor Graph Inc 46871 Bayside Pkwy Fremont CA 94538 USA

Incorporating semantics in modern projects can be time-consuming, rely on limited re-sources, and require expert knowledge to implement in a future-proof manner. Across many working groups and industrial fields, semantics continues to help solve data consolidation challenges;however, there are still emerging situations such as where modeling and ontology engineering efforts overlap, ongoing siloed data, or even continuing to create tech-debt for the future. Tools have been developed and researched that offer users assistance with the automation of select expert tasks to help streamline semantic artifact creation, understanding of design decisions, and domain data alignments. The semantic knowledge graph Generator and Recommendation Framework encourage semantic projects that are automated from the start to follow industry standards, enhanced with recommendations, and use components which make semantic artifacts more accessible for non-semantic experts. Based on that tool, this extension article presents several industry use cases and related discussion where semantic recommendations and deployment automation have assisted with project development, teaching about ontology structures, and gaining accessibility of semantic artifacts.

关键词： semantic project automation semantic recommender ontology recommender semantic knowledge graph semantic recommendation ontology education semantics education semantic decision support ontology accessibility adoption of semantics semantic case study, industry semantics

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Goal-Oriented Visual semantic Navigation Using semantic knowledge graph and Transformer

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 1647-1657页

作者： Wang, Zhongli Tian, Guohui Shandong Univ Sch Control Sci & Engn Jinan 250014 Peoples R China

When determining navigation actions, it is important to design effective visual and semantic representations of the observation scenes and robust navigation strategies. The paper proposes a goal-oriented visual semantic navigation method using semantic knowledge graph and transformer. Two kinds of knowledge graphs representing the location relationship between objects are constructed, namely current knowledge graph and prior knowledge graph. The pre-constructed prior knowledge graph is periodically updated by the current knowledge graph obtained in real time, and embedded into the semantic feature vector through graph convolutional network (GCN). The semantic features and extracted scene features are jointly embedded and stored, they are jointly fed into the transformer module to explore the spatio-temporal dependencies between objects in the environment. The navigation strategy is obtained from the Asynchronous Advantage Actor-Critic (A3C) model composed of Long-Short Term Memory (LSTM) and Multi-Layer Perception (MLP). Experiments show that the knowledge graph can significantly improve the navigation performance. More importantly, our experimental results show that our method can improve the generalization ability of navigation across novel scenes and novel objects. Video can be available at https://***/ZMjNvoK2rbY. Note to Practitioners-The motivation of this work is to develop an efficient visual semantic navigation method. Conventional navigation algorithms lack semantic information and learning ability, and can not adapt to the complex unknown environments. When semantic information is included in navigation, the location relationship between objects can be obtained as a prior knowledge, which can be combined with reinforcement learning to achieve autonomous navigation of agents. In this article, a knowledge graph representing the location relationships between objects has been constructed and regularly updated in real-time. The proposed visual semantic n

关键词： Visual semantic navigation semantic knowledge graph transformer reinforcement learning

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Modeling Recommendation and Project Deployment Automation for semantic knowledge graphs 17

Modeling Recommendation and Project Deployment Automation fo...

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17th IEEE International Conference on semantic Computing (ICSC)

作者： Ferguson, Holly Yu, Dan Kritzler, Mareike Siemens Smart Machine Vision Team Berkeley CA USA Mentor Graph Corp Integrated Circuits Verificat Berkeley CA USA

ISBN: (纸本)9781665482639

semantic technology continues to advance but adoption still has existing and emerging challenges due to varying amounts of individual knowledge for performing development tasks, limited semantic-expert resources, as well as fast-paced time lines to produce industry products. semantic modeling is usually time-consuming even for those with the expertise. Ad-hoc and rushed implementations may result in less ideal interoperability in downstream work, slowing down adoption of knowledge graphs in intelligent systems. The objective of this contribution is to use automation of certain expert tasks to propel semantic development efforts toward solutions that avoid compounding semantic tech debt for data alignment-based tasks and subsequent knowledge graph-based projects. This work aims to function in tandem with other existing internal data curation and deployment efforts to offer an automation-based framework and tool with results automatically deployable to a chosen project space, semantic editor, or triple store. This ultimately encourages development with artifacts that have already been assembled with all semantic dependencies to encourage using them alongside other industry standards. It also reduces expensive and redundant modeling efforts, helps to teach non-semantic experts about the implications of their modeling choices with recommendations and paradigm delineations, and eases remaining data alignment work so that results more easily follow semantic best practices.

关键词： semantic project automation semantic knowledge graph recommender ontology recommender semantic knowledge graph ontology education tool semantic graph education tool semantic knowledge graph decision support ontology accessibility industry adoption of semantics

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An Intuitive Interface for Technical Documentation Based on semantic knowledge graphs 7th

An Intuitive Interface for Technical Documentation Based on ...

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7th International Conference on Computer-Human Interaction Research and Applications, CHIRA 2023

作者： Loch, Frieder Stolze, Markus Department of Computer Science Ostschweizer Fachhochschule Rapperswil Switzerland

ISBN: (纸本)9783031494246

Maintaining technical documentation is a challenge. Products are becoming more complex, product lifecycles are getting shorter, and the number of product variants is increasing. Manuals that guide personnel in the use and maintenance of products are critical to their efficient and safe operation. Authoring system for technical documentation therefore increasingly apply semantic models to control the cost of maintaining technical documentation. Working with formal semantic structures is challenging for technical writers who usually work with plain, written text. This paper presents an intuitive interface for a semantic knowledge graph to facilitate the adoption and use of semantic models in technical documentation. The interface allows working with unstructured text and to postpone its semantification. Users can add semantic annotations in an iterative and incremental way. The interface was developed using a user-centered design process and subjected to an evaluation with technical writers. The results indicate that technical writers could use the prototype successfully and enjoyed the underlying concepts. Further iterations will extend the system and, for example, use artificial intelligence to suggest semantic links to improve the quality of the knowledge graph. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction semantic knowledge graph Technical documentation User-centered design

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graph-Based Visual-semantic Entanglement Network for Zero-Shot Image Recognition

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 2473-2487页

作者： Hu, Yang Wen, Guihua Chapman, Adriane Yang, Pei Luo, Mingnan Xu, Yingxue Dai, Dan Hall, Wendy South China Univ Technol Guangzhou 510006 Peoples R China Univ Southampton Southampton SO17 1BJ Hants England Univ Lincoln Lincoln LN6 7TS England

Zero-shot learning uses semantic attributes to connect the search space of unseen objects. In recent years, although the deep convolutional network brings powerful visual modeling capabilities to the ZSL task, its visual features have severe pattern inertia and lack of representation of semantic relationships, which leads to severe bias and ambiguity. In response to this, we propose the graph-based Visual-semantic Entanglement Network to conduct graph modeling of visual features, which is mapped to semantic attributes by using a knowledge graph, it contains several novel designs: 1. it establishes a multi-path entangled network with the convolutional neural network (CNN) and the graph convolutional network (GCN), which input the visual features from CNN to GCN to model the implicit semantic relations, then GCN feedback the graph modeled information to CNN features;2. it uses attribute word vectors as the target for the graph semantic modeling of GCN, which forms a self-consistent regression for graph modeling and supervise GCN to learn more personalized attribute relations;3. it fuses and supplements the hierarchical visual-semantic features refined by graph modeling into visual embedding. Our method outperforms state-of-the-art approaches on multiple representative ZSL datasets: AwA2, CUB, and SUN by promoting the semantic linkage modelling of visual features.

关键词： Visualization semantics Pipelines knowledge engineering Load modeling Task analysis Couplings Zero-shot learning visual-semantic modeling graph convolutional network semantic knowledge graph attribute word embedding

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Remote Sensing Image Interpretation With semantic graph-Based Methods: A Survey

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 4544-4558页

作者： Sun, Shengtao Dustdar, Schahram Ranjan, Rajiv Morgan, Graham Dong, Yusen Wang, Lizhe Yanshan Univ Dept Comp Sci & Engn Qinhuangdao 066004 Hebei Peoples R China TU Wien Comp Sci A-1040 Vienna Austria Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England China Univ Geosci Fac Earth Sci Wuhan 430079 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430079 Peoples R China

With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and semantic knowledge graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

关键词： semantics Remote sensing Ontologies Big Data Data models Earth Data mining Remote sensing (RS) image interpretation remotes sensing big data RS geological interpretation semantic graph-based method semantic knowledge graph

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Hierarchical aspect-based sentiment analysis using semantic capsuled multi-granular networks

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INFORMATION SYSTEMS 2025年 132卷

作者： Gracewell, Jeffin Raj, A. Arul Edwin Kalaivani, C. T. Renugadevi, R. Saveetha Engn Coll Chennai Tamil Nadu India Univ Tun Hussein Onn Parit Raja Malaysia Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India

In the ever-evolving domain of sentiment analysis, discerning intricate sentiments towards specific aspects and their sub-components within textual data has become pivotal. This paper introduces the semantic Capsuled Hierarchical Multi-Granular Network (SCH-MGN) model, an innovative approach explicitly designed for aspect-based sentiment analysis (ABSA) challenges. The SCH-MGN model is primed to evaluate sentiments at both macro (broader topics) and micro (detailed sub-aspects) hierarchical levels, offering a comprehensive sentiment evaluation spectrum. By integrating mechanisms like the semantic knowledge graph Attention Network (SKGAN) for targeted aspect extraction, Hierarchical Embedding Layers leveraging Multilingual BERT (mBERT), and advanced neural architectures including Recurrent Neural Networks (RNNs) and Temporal Convolutional Networks (TCNs), the model ensures a nuanced sentiment interpretation. The paper provides a meticulous dissection of the model's methodology, from tokenization and embedding to detailed sentiment extraction, accentuating its capability to offer granular sentiment interpretations. Empirical illustrations validate the model's proficiency in handling compound sentiments, cementing its potential as an indispensable tool for businesses, reviewers, and analysts. This groundbreaking approach to ABSA promises to redefine the granularity with which we understand and evaluate textual sentiments in diverse domains.

关键词： Aspect-based sentiment analysis Hierarchical sentiment evaluation semantic knowledge graph Capsule networks BERT embeddings Context preservation

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Federated semantic Learning Driven by Information Bottleneck for Task-Oriented Communications

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IEEE COMMUNICATIONS LETTERS 2023年第10期27卷 2652-2656页

作者： Wei, Hao Ni, Wanli Xu, Wenjun Wang, Fengyu Niyato, Dusit Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518066 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Jurong West 117583 Singapore

In this letter, we present a novel federated semantic learning (FedSem) framework to collaboratively train the semantic-channel encoders of multiple devices with the coordination of a base station-based semantic-channel decoder, which is more practical for task-oriented communications rather than performing the vanilla distributed learning merely at the user's side. Particularly, one of the most potential tasks, i.e., constructing semantic knowledge graph (SKG), is considered due to its powerful semantic structured representation and cross-modal comprehension capacity. In this case, the proposed FedSem can make full use of the distributed data and computing resources while protecting user privacy to directly parse images into SKGs. Furthermore, the information bottleneck theory is leveraged to drive the loss design by formalizing a rate-distortion tradeoff, which eliminates the redundancies of semantic features while maintaining the task-relevant information. Compared to the baselines, simulation results demonstrate that the proposed FedSem achieves better rate-distortion performance and convergence under different number of users and channel conditions.

关键词： semantics Task analysis Transmitters Receivers Training Feature extraction Decoding Federated semantic learning information bottleneck semantic knowledge graph task-oriented communications

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Ontology Data Insights and Alerts for Wildfire Protection 24

Ontology Data Insights and Alerts for Wildfire Protection

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13th Hellenic Conference on Artificial Intelligence

作者： Iacovou, Marios Kontogiannis, Stelios Avgerinakis, Konstantinos Catalink Ltd Nicosia Cyprus

ISBN: (纸本)9798400709821

This paper presents an improved approach to wildfire protection by integrating heterogeneous data sources under a novel fire detection ontology. The introduced technique automatically unifies data from IoT devices into a single semantic knowledge graph (SemKG), improving user experience by removing the need for users to examine several data sources. To make easier quick and well-informed decision-making, the system generates real-time notifications with different degrees of severity. The validation of the introduced technique has already taken place in various pilot sites of the SILVANUS H2020 project, which focus on developing a climate-resilient forest management platform to prevent and suppress wildfires, involving 49 partners from the European Union, Brazil, Indonesia, and Australia. In this paper, we demonstrate the effectiveness of this approach through scenarios of active fire detection and gas leakage.

关键词： Wildfire Protection SILVANUS Ontology IoT semantic knowledge graph Fire Detection Health Impact Monitoring

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Leveraging a cloud-native architecture to enable semantic interconnectedness of data for cyber threat intelligence

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第5期25卷 3629-3640页

作者： Ammi, Meryem Adedugbe, Oluwasegun Alharby, Fahad M. Benkhelifa, Elhadj Naif Arab Univ Secur Sci Riyadh Saudi Arabia Staffordshire Univ Smart Syst AI & Cybersecur Res Ctr Stoke On Trent ST4 2DE Staffs England

Cloud technologies have several merits including the elimination of cost incurred when traditional technologies are adopted. Despite the benefits, the cloud is still facing security challenges thereby calling for cyber threat intelligence capable of identifying threats and providing possible solutions. However, dependence on traditional security mechanisms and approaches for security solutions within cloud environments presents challenges. This calls for cloud-native solutions which leverages cloud features for design and development of solutions for data and applications hosted and running within the cloud. Past studies have suggested the adoption of semantic technologies for cloud-based security mechanisms. However, the semantic processing of data faces challenges of data interconnectedness due to aggregation of data from diverse heterogenous sources. Hence, this study proposes a cloud-native architecture capable of connecting security-related data from different sources in the cloud to enhance cyber threat intelligence. It presents a proof-of-concept implementation of the proposed solution on Amazon AWS cloud, within an auto-scaling group for scalability and across multiple availability zones for high availability.

关键词： Cyber threat intelligence Cyber Incident Response Cloud Computing semantic knowledge graph Data interconnectedness

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